Program

November 29

9:30

Marija: why are we here

10:00-10:45

Be ready to introduce yourself in 5 minutes, slides welcomed

11:00-11:30

Fredrik: Frontpage automation and personalisation

I’ll present the work we’ve done on automating and personalising the front page of Aftenposten. This work will be continued on other Schibsted newspapers in Norway and Sweden. Even though I don’t have time to edit the slides much from last presentation (for an entirely different crowd), I’ll also try discuss what ethical questions have been raised, and how we have implemented tooling to let the editors keep control, while still minimizing the work they do to groom the front page.

Algorithmic bias and polarization constitute an ever growing danger to our online media landscape. The consequences affect not only individuals themselves – who are often locked in the filter bubbles of their personal news feeds –, but also results in severe societal damage. The goal of this presentation is to show, suggest and discuss methods for quantifying different forms of bias – wanted and unwanted, natural and algorithmic – and to reduce the unwanted effects of such bias, while maintaining the benefits of a pluriform media landscape. During the presentation, I will show results from an analysis on perceived bias in the Chilean media, based on a recently published journal article.

In this talk I introduce epistemic justifications of democracy, show how interactions on social networks might undermine central premises of such justifications, and ask whether a tighter regulation of social media platforms can be justified. One might think that the right to a free press and media ties the hands of regulators. I will aim to show that this view is mistaken. By unpacking the core values underpinning media freedom, I show that setting limits to media freedom may be required to uphold the core values that underpin the normative ideal of free media.

November 30

09:30-10:00

Joris: Accountability by dialogue: A new approach to data protection
(based on joint work with Stéphanie van Gulijk, Tilburg School of Law)Current legal frameworks for data protection have a number of flaws. The notion of informed consent does not work in practice. Legislation covers personal data, but it doesn’t cover conditions on usage of data for certain purposes. Supervision is largely based on self-regulation. Regulatory agencies have little capacity. On the other hand, we observe that many business models are based on data about users. Users pay with their data. Access to data should therefore be seen as a counteroffer in a contract.In this paper we propose a different approach to data protection, based on the notion of computational accountability. The idea is to empower users to negotiate better terms and conditions in their contracts, monitor compliance, and challenge the organization in case of a breach of contract. So in addition to the current public law framework for data protection, we suggest to use private law as the legal framework of preference. To enable accountability over data protection, we foresee two kinds of functionality. (i) Tools that may help users negotiate sensible contracts that take data protection aspects into account. (ii) An infrastructure that monitors actual usage of data, detects possible breaches of contract and allows users to challenge the organization. In addition, we discuss the governance structure to enable effective enforcement.

In this presentation, I am going to discuss a series of diverse interdisciplinary research projects that I have been working on the last three years, which I believe can contribute substantially to the development of a project on machine ethics issues in modern journalism. More specifically, I am going to elaborate on papers I have published and others I am currently developing on the topics of: i) evolution of social networks, ii) online/offline interactions, iii) user behaviour on online and virtual spaces, as well as iv) privacy, rights and security.

We give an overview of technical, legal, and ethical possibilities and challenges in collecting digital trace data of mobile news consumption. The shift to online and especially mobile news consumption has led to the creation of unprecedented amounts of data on individual news consumption behavior. In theory, we can now know which respondents viewed (and liked, shared, and commented on) which news articles at which time. This allows a much more fine-grained study of media effects compared to the ‘classical’ method of linking media content to (panel) survey data (Scharkow & Bachl, 2017). For example, it enables a direct study of the effects of news algorithms and personalized news, letting us directly measure to what extent ‘filter bubbles’ really exist and what effect they have on political preferences (e.g. Bodo et al, 2017; Zuiderveen Borgesius et al., 2016). Although digital news consumption tracking is in its infancy in communication science, there are some previous studies that have tracked news consumption of consenting respondents on the desktop. These approaches are focused on desktop browsing, however, and more and more news is now consumed from mobile devices. Unfortunately, the methods that were used for tracking desktop news consumption cannot be simply adopted for tracking mobile news due to inherent technical differences between desktop and mobile systems. I will discuss the criteria and desirable aspects for a possible solution for mobile browsing and give an overview of different possibilities and discuss the extent to which each solution meets the listed criteria.

Helle: Journalism’s social contract: The principle of reciprocity in an online environment

13:30-14:00

Sjur & Truls: The legally mandated approximate language about AI

In light of the current explosion of application of machine learning in data
analysis and inference, we examine a particular challenge raised by the new
EU General Data Protection Regulation (GDPR). The challenge we address
pertains particularly to the demand that analyses of a person’s data must be
comprehensible to that person.
While there is a long tradition in viewing the world in terms of objects
and properties in intuitive ways, recent decades have entertained a tension
between more rule-based theories of mind (e.g., the representational theory
of mind) and more holistic approaches (e.g., connectionism). While both
approaches have merit, one seems to depart too much from a classical
understanding of “knowing” to adequately satisfy the imminent legal realisty,
and the other seems to be incapable of adequately capturing modern data
analysis (as of yet).
As a solution to this predicament we propose a pragmatic compromise
based on argumentation theory which seems to be able to provide a
solid foundation in classical concepts, while at the same time permitting
enthymematic presuppositions. We argue that developing a framework for
explaining machine behavior in terms of abstract argumentation theory can
address this dilemma.